There is an ever increasing number of rule learning algorithms and tools for automatic knowledge base (KB) construction. These tools often produce weighted rules and facts that make up a probabilistic KB (PKB). In such a PKB, probabilistic inference is used in order to perform marginal inference, consistency checking and other tasks. However, in general, inference is known to be intractable. Hence, recently, there are a number of studies aimed at lifting (making tractable or approximating) inference by exploiting symmetries in the structure of a PKB. These studies alleviate grounding entirely a given PKB which can generate a sizable factor graph for inference (e.g. to compute the probability of a query). In line with this, we propose a novel technique to automatically partition rules based on their structure for efficient parallel grounding. In addition, we perform query expansion so as to generate a factor graph small enough to be used for efficient probability computation. We present a novel approximate marginal inference algorithm that uses N-hop subgraph extraction and query expansion. Moreover, we show that our system is much faster than state-of-the-art systems.